Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

Kavita Aggarwal; Saurav Patra; Viyatprajna Acharya; Mahesh Agrawal; Sri Krushna Mahapatra

doi:10.18203/2349-3933.ijam20194155

Authors

Kavita Aggarwal Department of Biochemistry, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
Saurav Patra Department of Biochemistry, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India
Viyatprajna Acharya Department of Biochemistry, Institute of Medical Sciences & SUM Hospital, Bhubaneswar Siksha ‘O’ Anusandhan University, Odisha, India
Mahesh Agrawal Department of Anaesthesiology, Utkal Institute of Medical Sciences, Bhubaneswar, Odisha, India
Sri Krushna Mahapatra Department of Biochemistry, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India

DOI:

https://doi.org/10.18203/2349-3933.ijam20194155

Keywords:

Bias, Imprecision, Method decision chart, Quality control, Quality Goal Index, Sigma metrics

Abstract

Background: Six sigma is a powerful tool which can be used by laboratories for assessing the method quality, optimizing Quality Control (QC) procedure, change the number of rules applied, and frequency of controls run .The aim of this study was to quantify the defects or errors in the analytical phase of laboratory testing by sigma metrics and then represent the sigma value in Method Decision Chart.

Methods: A retrospective study was conducted in a tertiary care hospital in Bhubaneswar, India. The clinical chemistry laboratory has been NABL accredited for the past 5 years and strictly quality checked. Internal and external quality control data was collected for a period of six months from January - June 2018 for 20 biochemical analytes. Sigma metrics for each parameter was calculated and plotted on method decision chart.

Results: The sigma metrics for level 2 indicated that 6 out of the 20 analytes qualified Six Sigma quality performance. Of these seven analytes failed to meet minimum sigma quality performance with metrics less than three and another seven analytes performance with sigma metrics was between three and six. For level 3, the data collected indicated that seven out of 20 analytes qualified Six Sigma quality performance, six analytes had sigma metrics less than 3 and seven analytes had sigma metrics between 3 and 6.

Conclusion: In our study Sigma value was highest for amylase and lowest for potassium. Use of alternative methods and/ or change of reagents can be done for potassium to bring the sigma value within an acceptable range.

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